When handling audio signals, such as speech signals, at an encoder of a transmitting unit, the audio signals are represented digitally in a compressed form using for example Linear Predictive Coding, LPC. As LPC coefficients are sensitive to distortions, which may occur to a signal transmitted in a communication network from a transmitting unit to a receiving unit, the LPC coefficients might be transformed to envelope representation coefficients at the encoder. Further, the envelope representation coefficients may be compressed, i.e. coded, in order to save bandwidth over the communication interface between the transmitting unit and the receiving unit.
A further use of the spectral envelope is to apply a mean removed normalized frequency envelope to scale a frequency domain signal prior to quantization, based on a quantized spectral envelope in order to control the frequency location and magnitude of the spectral line quantization errors introduced in the spectral line quantization for those frequency locations. The mean removed normalized frequency envelope may be represented as a vector of scale factors.
LSF coefficients provide a compact representation of a spectral envelope, especially suited for speech signals. LSF coefficients are used in speech and audio coders to represent and transmit the envelope of the signal to be coded. The LSFs are a representation typically based on linear prediction. The LSFs comprise an ordered set of angles in the range from 0 to pi, or equivalently a set of frequencies from 0 to Fs/2, where Fs is the sampling frequency of the time domain signal. The LSF coefficients can be quantized on the encoder side and are then sent to the decoder side. LSF coefficients are robust to quantization errors due to their ordering property. As a further benefit, the input LSF coefficient values are easily used to weigh the quantization error for each individual LSF coefficient, a weighing principle which coincides well with a wish to reduce the codec quantization error more in perceptually important frequency areas than in less important areas.
Legacy methods, such as AMR-WB (Adaptive Multi-Rate Wide Band), use a large stored codebook or several medium sized codebooks in several stages, such as Multistage Vector Quantizer (MSVQ) or Split MSVQ, for LSF, or Immittance Spectral Frequencies (ISF), quantization, and typically make an exhaustive search in codebooks that is computationally costly.
Alternatively, an algorithmic VQ can be used, e.g. in EVS (Enhanced Voice Service) a scaled D8+ lattice VQ is used which applies a shaped lattice to encode the LSF coefficients. The benefit of using a structured lattice VQ is that the search in codebooks may be simplified and the storage requirements for codebooks may be reduced, as the structured nature of algorithmic Lattice VQs can be used. Other examples of lattices are D8, RE8. In some EVS mode of operation, Trellis Coded Quantization, TCQ, is employed for LSF quantization. TCQ is also a structured algorithmic VQ.
There is an interest to achieve an efficient compression technique requiring low computational complexity at the encoder.